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The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways (“the survival phenotype”) or the EGFR, KRAS (G12V), RAF1, and BAD pathways (“the growth phenotype”). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.


This article was originally published in Journal, volume 9, in 2017.

Dr. Moom Roosan was known as Mumtahena Rahman at the time of publication.

13073_2017_429_MOESM1_ESM.pdf (2514 kB)
Supplemental results, figures and tables. (PDF 2514 kb)

13073_2017_429_MOESM2_ESM.xlsx (149 kB)
The cell lines used in the independent drug assay and the Western blotting experiments, and the drug doses and negative log EC50 values for the independent drug assay. (XLSX 149 kb)

13073_2017_429_MOESM3_ESM.xls (1399 kB)
Full results from the GSVA gene set enrichment analysis for the HER2, IGF1R, AKT, BAD, EGFR, KRAS, and RAF1 signatures. (XLS 1399 kb)

13073_2017_429_MOESM4_ESM.xlsx (30 kB)
Optimized gene lists for the AKT, IGF1R, BAD, EGFR, HER2, KRAS, and RAF1 signatures. (XLSX 30 kb)

13073_2017_429_MOESM5_ESM.xlsx (152 kB)
Scaled ASSIGN pathway activity predictions, phenotype, and k-means cluster calls for TCGA and ICBP samples. (XLSX 152 kb)


The authors

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This work is licensed under a Creative Commons Attribution 4.0 License.



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